Parametric models (regression formulas) are often relied upon by parts purchasers and/or suppliers during negotiations to predict prices based on observed prices and part attributes. Indeed, the value of parametric price-prediction models has become so recognized that both purchasers and suppliers are now developing parametric price-prediction modeling processes for use during future negotiations.
Although other predicted price formulas are possible, predicted prices are generally a linear function of the objective part attributes. Some of the various part attributes that may be considered include physical characteristics (e.g., material, length, weight, etc.), functional characteristics (e.g., passenger door versus landing gear door, etc.), among numerous other attribute possibilities. In addition, attributes that are functions of other attributes (e.g., Area=Width*Length) may also be used in a parametric price-prediction model. Determining which attributes to collect and calculate when constructing a model is a very complex and complicated task for the modeler and others involved in the parametric modeling process.
In addition to the attributes that tend to determine costs, purchased parts also reflect the competitive pressures of a free market economy. Thus, another goal in the development of a parametric pricing model is to simulate a competitive marketplace and use this simulation to compare or rank suppliers.
Data complexity for parts and part assemblies continues to increase. Indeed, it is not uncommon for part assemblies to require consideration of about one-thousand (1,000) attributes. Due to the complexity of the data associated with such part assemblies, a substantial amount of time is needed for developing and evaluating the more challenging parametric price modeling of part assemblies.
Meanwhile, and despite the increasing data complexity associated with both parts and part assemblies, staff and modeling time available for parametric price modeling has decreased. Moreover, re-pricing parts over time also requires new models based on updated data, which only further adds to the disparity between the modeling effort required and the limited amount of available staff and modeling time.
Developing candidate models with existing known methods and tools typically requires at least about one-half (0.50) hour per candidate model. Even with 20 to 50 attributes in the data, modelers will construct and manually evaluate hundreds of candidate models. Understandably then, developing price-prediction models for datasets including hundreds of attributes, which can generate many hundreds or even thousands of candidate models, can be especially time consuming. Moreover, after expending the time and resources to develop a candidate model, the candidate model may ultimately be rejected because it fails simple reasonability tests (e.g., too high a correlation between two attributes, an unreasonably large number of negative predicted prices, etc.) applied after the model has been constructed. In the past, this review has been done through manual review by modelers.
In addition, a predominantly manual selection process may overlook a good candidate model because tracking model development is difficult with existing tools and resources. In addition, models must be reconstructed when data changes because data changes can render interim modeling and price prediction results useless. As a result of such data changes, a substantial amount of time is currently spent on updating and revalidating parametric pricing models to retain full credibility.
Computerized statistical analysis tools do exist, such as Statistica® statistical analysis software available from Statistica, Inc. Corporation of Gaithersburg, Md. and JMP® statistical analysis software available from SAS Institute Inc. Corporation of Cary, N.C. However, the existing computerized tools are manually driven, require significant user interaction, and produce one candidate model at a time such that the regressions are considered in isolation and reviewed individually by the modeler. Although the existing computerized statistical analysis tools allow for construction of models without writing software, the existing tools do not keep track of candidate models well and require each model to be constructed under significant human control. Accordingly, the current approach to developing and testing candidate models is time consuming especially when hundreds of candidate models must be tested or retested as data changes. Moreover, the current approach is not possible when thousands or even hundreds of thousands of candidate models must be tested or retested.